Classic Computer Science Problems in Python
Classic Computer Science Problems in Python deepens your knowledge of problem solving techniques from the realm of computer science by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems!
Table of Contents takes you straight to the bookdetailed table of contents
0 Introduction
0.1 Why Python?
0.2 What is a classic computer science problem?
0.3 What kinds of problems are in this book?
0.4 Who is this book for?
0.5 Python versioning, source code repository, and type hints
0.6 No graphics, no UI code, just the standard library
0.7 Part of a series
1 Small problems
1.1 The Fibonacci sequence
1.1.1 A first recursive attempt
1.1.2 Utilizing base cases
1.1.3 Memoization to the rescue
1.1.4 Automatic memoization
1.1.5 Keep it simple, Fibonacci
1.1.6 Generating Fibonacci numbers with a generator
1.2 Trivial compression
1.3 Unbreakable encryption
1.3.1 Getting the data in order
1.3.2 Encrypting and decrypting
1.4 Calculating pi
1.5 The Towers of Hanoi
1.5.1 Modeling the towers
1.5.2 Solving The Towers of Hanoi
1.6 Real-world applications
1.7 Exercises
2 Search problems
2.1 DNA search
2.1.1 Storing DNA
2.1.2 Linear search
2.1.3 Binary search
2.1.4 A generic example
2.2 Maze solving
2.2.1 Generating a random maze
2.2.2 Miscellaneous maze minutiae
2.2.3 Depth-first search
2.2.4 Breadth-first search
2.2.5 A* search
2.3 Missionaries and cannibals
2.3.1 Representing the problem
2.3.2 Solving
2.4 Real-world applications
2.5 Exercises
3 Constraint-satisfaction problems
3.1 Building a constraint-satisfaction problem framework
3.2 The Australian map-coloring problem
3.3 The eight queens problem
3.4 Word search
3.5 SEND+MORE=MONEY
3.6 Circuit board layout
3.7 Real-world applications
3.8 Exercises
4 Graph problems
4.1 A map as a graph
4.2 Building a graph framework
4.2.1 Working with Edge and Graph
4.3 Finding the shortest path
4.3.1 Revisiting breadth-first search (BFS)
4.4 Minimizing the cost of building the network
4.4.1 Workings with weights
4.4.2 Finding the minimum spanning tree
4.5 Finding shortest paths in a weighted graph
4.5.1 Dijkstra’s algorithm
4.6 Real-world applications
4.7 Exercises
5 Genetic algorithms
5.1 Biological background
5.2 A generic genetic algorithm
5.3 A naive test
5.4 SEND+MORE=MONEY revisited
5.5 Optimizing List Compression
5.6 Challenges for genetic algorithms
5.7 Real-world applications
5.8 Exercises
6 K-means clustering
6.1 Preliminaries
6.2 The k-means clustering algorithm
6.3 Clustering governors by age and longitude
6.4 Clustering Michael Jackson albums by length
6.5 K-means clustering problems and extensions
6.6 Real-world applications
6.7 Exercises
7 Fairly Simple Neural Networks
8 Adversarial Search
9 Miscellaneous Problems
Appendixes
Appendix A: Glossary
Appendix B: Further Resources
About the Technology
Don't just learn another language. Become a better programmer instead! Python is used everywhere for web applications, data munging, and powerful machine learning applications. Even problems that seem new or unique stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills, and you’ll be ready to use Python for optimization problems, AI, machine learning, and the other challenges you’ll face as you grow your skill as a programmer.About the book
Classic Computer Science Problems in Python presents dozens of coding challenges, ranging from simple tasks like finding items in a list with a binary search algorithm to clustering data using k-means. For each carefully-selected problem, you’ll find an artful solution along with a clear explanation both of how to think about the problem and how to apply your new skill to other similar scenarios. You’ll appreciate author David Kopec’s amazing ability to connect the core disciplines of computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview!Based on David’s book Classic Computer Problems in Swift, this book offers Python-based examples to the same core problems as well as a new chapter on Adversarial Search.
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